In this paper, we conduct a detailed study of the
YouTube CDN with a view to understanding the mechanisms
and policies used to determine which data centers users download
video from. Our analysis is conducted using week-long datasets
simultaneously collected from the edge of five networks - two
university campuses and three ISP networks - located in three
different countries. We employ state-of-the-art delay-based geolo-
cation techniques to find the geographical location of YouTube
servers. A unique aspect of our work is that we perform our
analysis on groups of related YouTube flows. This enables us to
infer key aspects of the system design that would be difficult
to glean by considering individual flows in isolation. Our results
reveal that while the RTT between users and data centers plays a
role in the video server selection process, a variety of other factors
may influence this selection including load-balancing, diurnal
effects, variations across DNS servers within a network, limited
availability of rarely accessed video, and the need to alleviate
hot-spots that may arise due to popular video content.


-Content distribution networks; Web and internet services

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